A more general methodology that exploits the Maximum Entropy Principle \((ME)\)  [1]} combined with a constraint-based model, in order to formulate a probabilistic description of the metabolic state of cells in the culture, has been used recently [2]}, [3]}, [4]}, [5]}.
In particular, it has been shown that \(ME\)  distributions provide a better fit to measured flux observables than plain \(FBA\)  models [3]}.
Also, \(ME\) -derived growth rate distributions have been compared to experiments with good results, using single-cell data at different sub-inhibitory antibiotic concentrations [3]}.
A more recent formulation exploits the availability of experimental data from the culture, to further limit the available solution space for the metabolism [2]}.
In practice, the authors of [2]} not only fix the growth rate, but all the experimental fluxes.
This approach however, leaves open relevant questions: i) What is the minimum number of fluxes that we need to fix to have a proper description of the system? ii) Is it preferable to fix some particular fluxes instead of others? If we fix too many fluxes, we leave too few of them to test the predictions derived from the inference.
Moreover, we also increase the risk of introducing in the inference process unnecessary biases that may come from uncontrolled experimental errors made in the measurement process.
